# Large Animal Core (Core 1)

> **NIH NIH P01** · MASSACHUSETTS GENERAL HOSPITAL · 2024 · $633,640

## Abstract

Large Animal Core Abstract
 We have developed the first validated, gyrencephalic, large animal model of post-
traumatic epilepsy achieving extreme, long-term video EEG digital radiotelemetry, and we have
developed cutting-edge technology allowing single cell imaging in the largest species to date.
Building on our 5 years’ experience, the Large Animal Core aims to transduce the cortex of
swine with or without post-traumatic epilepsy with proteins that report intraneuronal
concentrations of chloride and calcium, surgically implant and maintain imaging ports for the
Microscopy Core allowing for serial, 2-photon imaging, and produce and archive
video/electrographic data and collect and store tissues of swine with or without post-traumatic
epilepsy. In particular, the Large Animal Core will provide surgeries (implant the ECoG, AAV
injection, install the imaging ports, cortical impact, inject extracellular chloride dye, 2-photon
imaging), record video ECoG with real-time analysis, collect tissues, animal husbandry
procedures, regulatory compliance, and facilitation of swine housing resources. The Core
Director and staff will participate in weekly meetings regarding administrative and scientific
matters such as any problem solving any issues that arise, convulsion screening, and
presentations of data.

## Key facts

- **NIH application ID:** 10896222
- **Project number:** 5P01NS127769-02
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Beth A Costine-Bartell
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $633,640
- **Award type:** 5
- **Project period:** 2023-08-01 → 2028-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10896222

## Citation

> US National Institutes of Health, RePORTER application 10896222, Large Animal Core (Core 1) (5P01NS127769-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10896222. Licensed CC0.

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